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It is with great excitement that we present this book, Genetic Tools for Improving Livestock Performance, which presents a comprehensive overview of the application of genomics within the Livestock industry and Animal Husbandry practices in the national scenario Genomics is an incredibly exciting field which is developing at a rapid rate, and this book serves to present some of the latest advancements in the field while also delving into its basic concepts. This book aims to explore the full gamut of genomic advancement in the animal-breeding sector. The potential use of genomic technology in livestock and poultry breeding is tremendous, greatly enhancing the ability of farmers to produce healthier and more resistant livestock and poultry. By using genetic information, breeders can select animals with specific traits, allowing them to produce higher quality products in a more efficient manner. Consequently, this technology has the potential to revolutionize the industry, resulting in improved yields and profits while also improving animal welfare. Within these pages, readers will find information on the many new methods that have been developed to analyze genetic markers and to identify desired traits in animals. The book is aimed at a wide range of readers, from those with a basic understanding of genetics to those with more detailed knowledge as both the basics of genomics and its more advanced applications, such as the application of genomics to the identification of specific traits in breeds and the development of new breeds etc. has been covered in this book. By examining the areas of genotyping, SNP (single nucleotide polymorphism) analysis, and high-density direct genomic selection (DNA-based selection), readers will gain a better understanding of how genomic technology can be utilized within the field.

Genomics is the study of whole genomes of organisms, it uses a combination of DNA sequencing methods, bioinformatics to sequence, assemble, analyse the structure and function of genomes. In another words, it can be defined as it is the study of the structure, function, and inheritance of the genome of an organism. Moreover, genomics focuses on interactions between loci and alleles within the genome and other interactions such as epistasis, pleiotropy and heterosis. Genomics is divided into two basic areas: structural genomics, characterizing the physical nature of whole genomes; and functional genomics, characterizing the transcriptome (the entire range of transcripts produced by a given organism) and the proteome (the entire array of encoded proteins). Most biological traits are controlled by a large number of genes, which, in addition to their additive effects, may interact with each other, and their expression might be altered based on a variety of environmental effects. Researchers who were among the first to use molecular genetics to the study of cattle began by employing DNA markers to locate genes or sections of the genome affecting features of interest. For instance, identifying a group of genes based on Genome wide association study (GWAS) provides evidence that a causal relationship between a phenotype and a single nucleotide polymorphisms (SNPs) may exist. These findings of GWAS, however, do not improve our understanding of complex traits in which many pathways may be involved. The growing use of genotyping technologies has generated a large quantity of genomic information on the dairy population. Since the draft sequence of the bovine genome was published in 2009 (Zimin et al., 2009), whole-genome sequence data of cattle have also become available.

Genetic improvement of farm animal species is accelerating in developed countries with adoption of Genomic selection (GS) due to availability of complete reference genome sequence i.e. gene sequences that can be used to compare and study animal genetic traits. GS has been considered most promising for genetic improvement of the complex traits controlled by many genes (Quantitative traits) each with minor effects as using the conventional methods of selection and breeding the rate of genetic gain per generation is very slow with respect to human demand for livestock products. With this method, animals or plants can be selected for selective breeding on the basis of their genetic merit predicted by whole genome molecular markers spanning over the entire genome. Genomic selection may allow the identification of superior individuals for traits such as increased disease resistance which are not currently considered in animal breeding plans because of technical difficulties. The method can predict the genetic merit of young bulls with greater accuracy as compared to convention methods of genetic merit estimation. Genetic gain (ΔG) in animal breeding programs depends upon the intensity of selection (i), accuracy of predictions (r), genetic variance (σ2g) and generation interval (IG): ΔG = i * r * σ2g / IG (Ibañez Escriche & Gonzalez-Recio, 2011).

Introduction The expanding number of people around the world necessitates an increase in the amount of food that is yielded. As stated by the Food and Agriculture Organization of the United Nations (FAO), global food production ought to be ramped up by a factor of two in the next few years in to meet the requirements of the growing global population in terms of consumption (FAO, 2006). This is because the global population is expected to continue growing at a rate of approximately one person every two seconds until the year 2050. It is possible that the predicted production disparity could be filled by making improvements in genetics, animal health, and the excellence of animal husbandry. The production of animals has expanded significantly over the course of the last several decades as a direct result of the application of quantitative genetics in animal breeding. However, due to technological limitations, the use of genetic markers in breeding programmes has only been implemented to a relatively small extent (Deng et al., 2016). The concept of genomic prediction, which was first introduced approximately twenty years ago, has completely transformed the planning and execution of animal breeding operations.

Introduction Due to global population growth, rising wages, and urbanisation, the demand for animal products has been expanding quickly in many areas of the world (FAO, 2018). Meeting the rising demand for animal products, which is expected to reach 8.6 billion by 2030 (FAO, 2018) as the world’s population is projected to increase by 80 million per year (Preisinger, 2012), is one of the biggest difficulties facing modern farm animal industry (Eggen, 2012). Animal products and value-added foods are being consumed as a result of urbanisation and income growth (Hoffmann, 2005; FAO, 2011). By 2050, the world’s population will be close to 10 billion, even as the economic conditions continue to improve the demand for animal products will significantly rise. In order to boost animal production, it will be necessary to acquire a more in-depth understanding of animal biology through genomics and other related f ields of study. Understanding animal physiology remains a significant problem in the context of sustainable agriculture and animal husbandry, particularly for developing animal farming systems that cater to the new and diversified consumer demand. Livestock and poultry productivity has been greatly increased through traditional selection based on genetic merit estimated from phenotypic and pedigree data. Most breeding plans do not take population impacts on genetic diversity into consideration, and selection is geared more towards the best genetic response for the upcoming generation than the best long-term response (Meuwissen, 1997). Breeders have employed genetic analyses to find superior animals for more than 40 years.

The overall number of animals kept as livestock in India comes to 535.78 million, making it the country with the greatest livestock population. According to the 20th Livestock Census, India has a total bovine population of 302.79 Million. This includes Cattle, Buffalo, Mithun, and Yak. There are a total of 109.85 million buffaloes living in the country. The number of goats in the country currently stands at 148.88, while the total number of sheep in the country is 74.26 Million. Most of the breeds in India have evolved naturally through adaptation to varied agro-ecological conditions and have generally been named after their place of origin or on the basis of prominent morphological characteristics. The number of registered breeds in India has increased over time. Majority of livestock breeds are reared by smallholder farmers are of non-descript type. Indiscriminate crossbreeding is extensively occurs resulting in herds of females that may have very complex ancestries.

Livestock is an integral component of Indian agriculture which is primarily a mixed crop-livestock farming system and plays an important role in the socio- economic life of India. Livestock contributes significantly to the growth and development of agricultural sector by providing a rich source of high quality products such as milk, meat and eggs and by supporting livelihood of more than two third of rural population. Livestock has a crucial and rapidly increasing role infood security and economic stability worldwide Livestock sector contributes for 4.11% of GDP and 25.6% of total Agriculture GDP. The importance of livestock has been undergoing a steady transformation with the contribution of livestock in total agriculture and allied sector Gross Value Added increasing from 24.32% in 2014-15 to 28.63% in 2018-19. Owing to factors such as population and economic growth, urbanization, improvements in transportation and storage practices,its future importanceformeeting human nutritional requirements of food and fiberand a major contributor to agricultural GDP is evident. To alleviate the concerns raised by a rapidly growing demand of food security and the need of ensuring sustainability of natural resources, we need to better understandthe ways which wouldspeed up commercialization of livestock production so that it can contribute effectively to the bio-economy. Livestock performance and productivity can be improved by adopting proper diet and feeding practices, improved farm management and disease controlstrategies.

People all around the world have been conducting genetic association studies for a long time. Still the literature to practically understand the statistical analysis of such data using software packages is scarce. Statistical analysis of molecular data is a vast topic. So we will be undertaking one of the important basic and preliminary topics “Statistical analysis of genetic association studies among unrelated individuals” in this chapter. Overall, we will be discussing population association studies in which qualitative and quantitative data on unrelated individuals for various economic traits is collected and these individuals are also typed at a number of Single Nucleotide Polymorphism (SNP) markers. This excludes family-based association studies, admixture mapping or linkage studies. Various software packages used in this chapter to analyze molecular data of a population are given below in Table 1.

Background Sheep and goats are the poor man’s cow because of the lower capital investment, and production costs. Also supplies milk quantities that are suitable for immediate household consumption thereby reducing problems of milk storage and marketing. Small ruminants, goats and sheep, mainly localized in the arid, semi-arid and mountainous areas of the country, have multi-faceted utility in terms of wool, meat, milk, skins, (mo)hair/pelts, manure and have also acquired socio-cultural niches in many communities (Kosgey and Okeyo, 2007). Goat and sheep milk has many advantages over other livestock species’ milk being easy in digestion, non-allergic, naturally homogenized, improves immunity as it has antifungal and anti-bacterial properties. However, rearing small ruminants for meat is more popular among farmers as price of chevon and mutton are very high, continuously increasing over the years and sale of male for meat is easy, steady and accessible at farmer’s place or local places unlike milk, skin and fibre. Thus, small ruminants (SR) occupy a promising agrarian and economic niche in low to medium input production systems, not suitable for growing crops and dairy farming (Devendra and Chantalakhana, 2002), mostly driven by small and marginal farmers and landless laborers. Their meat and milk production represents 4.8% and 3.4% of the total meat and milk produced, respectively, in the world.

Artificial intelligence (AI) uses multiple technologies that equip machines to sense, comprehend, plan, act, and learn with human-like levels of intelligence. Fundamentally, AI systems perceive environments, recognize objects, contribute to decision making, solve complex problems, learn from past experiences, and imitate patterns. AI technologies such as deep learning allow computer systems to understand a problem, learn from examples, and make predictions. The AI has a tremendous potential to solve the diverse application problems in the field of domestic animal breeding and management. Till date, most of the studies have been focused on animal behaviour (Valletta, et al., 2017), cognition (Lake et al., 2017), animal welfare (Neethirajan, 2021), individual identification (Norouzzadeh et al., 2018), growth or body condition scoring (Zin et al., 2020), disease detection and its monitoring (Kumar et al., 2021), precision farm management (Radun et al., 2021) etc. particularly in the pigs, poultry and cattle species. These AI based applications primarily involves data generation, data processing, use of gadgets / IoT devices, development of smart machine learning algorithms and their integration to attain precision to solve various issues in animal husbandry. Having said that, very few works have been carried out with respect to applications of AI in domestic animal breeding, and major problems like breed identification, genomics- based animal selection criteria and breeding decision, are yet to be automated. Moreover, there are some bottlenecks in application of AI in animal breeding, like the lower accuracy of a decision/outcome and large cost involved in creating AI based infrastructure.

Artificial Intelligence (AI) is a discipline of computer science that now days affecting evary aspect of day to day life. It has the potential to greatly improve animal breeding programs in India by providing more accurate and efficient selection methods. Some of the avenues and possibilities of using AI in animal breeding in India includes Predictive analytics: AI can be used to develop optimization models that take into account a variety of factors, such as genetics, phenotypic traits, and environmental conditions, to identify the best breeding strategies. AI can be used to analyze large amounts of data on animal genetics, phenotypic traits, and environmental factors to predict the likelihood of an animal producing offspring with desired traits. This can help breeders make more informed decisions about which animals to be selected for breeding. AI algorithms can be trained to accurately identify diseases in animals by analyzing imaging data, such as X-rays and MRI scans, as well as blood and tissue samples which in turn facilitate veterinarians to make more accurate diagnoses, improving treatment outcomes. AI can also improved feed efficiency by analyzing the feed consumption and nutrient utilization data to identify patterns and trends that can be further utilized to improve feed efficiency, reduced feed costs and increased production efficiency.

Introduction The sex of the animal is one of the most easily observable attributes of a livestock population. The importance of the sex ratio lies in the fact that it sets the future rates of fertility, mortality, and migration. In livestock, male animals are required in fewer numbers compared to females for breeding. So, for profitable management of a farm, the sex ratio has been fixed for each species. But because of the natural 50:50 chance of both types of sperms to fertilize an ovum, this preferred sex ratio is never achieved. In order to reach this economical ratio, additional efforts must be done in the form of culling. Moreover, in monotocous species with nearly one year gestation period, a long wait is sometimes required for getting the offspring of particular sex. In dairy farming, the number of female offspring born is always an asset to the farm. For the species reared for meat, the males are often avoided due to the intense odor of their meat and handling difficulty while rearing. But when there is shortage of good quality breeding bulls, the sorted sperms will be helpful for producing the elite bulls for breeding. If we can produce only the animal of the desired gender, we can eliminate split-sex feeding, reduce problems related to animal welfare and castration, improve production efficiencies. Additionally, the costs of the progeny testing programme and embryo transfer are reduced as a result, and the value of genetic markers is increased. Apart from the livestock species, sex pre-selection is also important in breeding of companion animals, zoo animals and endangered species.

Introduction Raising milk and meat production are long-standing global issues. Researchers are learning that present breeding efforts won't be sufficient to meet anticipated future food demands given the current climate scenario. The animals that influence the overall yield of the field must be screened as soon as possible. In order to build more successful modern breeding programmes, high-throughput phenotyping is currently being included into conventional breeding operations. Despite the public accessibility of their genetic information online, there is still a lack of phenotypic data on the genomes of various animal breeds because environmental factors make it more difficult to define phenotypes and increase the likelihood of measurement error (Rahaman et al., 2015). Genomic selection (GS) is a strategy that predicts the genomic estimated breeding values of lines in a breeding population using information from genome-wide markers (Meuwissen et al., 2001). When compared to other conventional methods like marker-assisted selection (MAS), genomic selection has some inherent advantages. These include boosting genetic gain by reducing breeding cycles and acquiring minor effect loci based on markers dispersed throughout the entire target genome. Reliable phenotypes and genotyping are both necessary for the training of accurate prediction models for genomic selection. Phenotyping is regarded as a significant factor restricting genetic improvements in animal breeding due to high labour and time expenditures. Traditional approaches of phenotyping are prohibitively expensive, time-consuming, and slow.

Energy is essential for each and every single biological reaction in the body of an individual. Production, reproduction and physiological functions of an animal depend on its body state and utilization of energy from dietary source and its body reserves. Energy reserves are determined primarily by the relative amount of fat accessible as an energy source and are indicative of health condition. Adipose tissue (AT) is a primary reservoir of energy and recognized as an immunologically and endocrinologically active tissue. Cattle have the ability to utilize body reserve to meet energy demand due to negative energy balance during peripartum period, which is crucial for maintaining their health lactation and neonates. There are several methods to measure tissue reserves like body weight, metabolic and hormonal factors, respiratory calorimeter body water by dilution with D2 O, estimation of fat cells diameter, ultrasound techniques etc. Body weight is not a good indicator as it is affected by different factors such as; age, parity, gestation, body frame, gastrointestinal contents weight of vital organs and shows a greater variation in measuring fat stores. Other methods are inapplicable in field conditions as they are costly, time consuming and need laboratory facilities and skilled technicians. However Body Condition Score (BCS) is an easier and the most practical method, which is an assessment of thickness of fat cover, prominence of bone of tail and head region.

India is bestowed with a wide variety of livestock species which forms the backbone of the economy of majority of the farmers. Cattle are one of the important species domesticated by man mainly for their draftability in agricultural operations. Rearing of cattle is an integral part of the Indian agriculture as it is always considered as a subsidiary enterprise of the agriculture farming. The indigenous cattle are well known for their adaptability to the harsh tropical climatic conditions, ability to thrive in the resource poor production systems, better feed efficiency and disease resistance. Barring few dairy breeds, majority of the Indigenous cattle breeds are poor in their genetic potential for milk production. Considering the ever increasing demand from milk , the country has implemented many cattle improvement programmes like Central Herd Registration Scheme (CHRS), Intensive Cattle Development Programme (ICDP), All India Coordinated Research Project (AICRP) on Cattle National Programme for Bovine Breeding and Dairy Development (NPBBD), National Livestock Mission, National Project for Cattle and Buffalo Breeding (NPCBB), Rashtriya Gokul Mission (RGM) etc., for improving the milk productivity of cattle.

Introduction The key factor of sustainability in the animal sector depends upon genetic improvement in domestic livestock. There are many reproductive and molecular biotechnologies used for the genetic improvement of domestic livestock and they are synergistically coupled with the breeding program in distinct breeding targets. With the help of gene editing techniques (e.g. CRISPR etc.). To improve the specific traits of interest scientists can add, delete or replace the nucleotides (e.g. milk production, meat production, disease resistance, etc.) in less than one generation. Gene editing with the conventional breeding program in livestock is an important factor for genetic improvement for increasing the rate of genetic gain. In the present topic, here, we delved into the most important reproductive and molecular technologies used in livestock genetic improvement, as well as the most effective ways for incorporating gene editing into existing genetic improvement programmes and targeting specific traits for enhancement. The improvement in genetic level is a most powerful part of sustainable animal development because it is permanent and the effects are long-lasting and cumulative. Genetic advancements produced in one generation are passed on to the following, unlike dietary and animal health treatments, which are a continuous process. The many livestock operations that involve chemical and mechanical methods such as dehorning requires time, worker, money, animal safety, etc., but their genetic alternation of the concerned gene for the polled or hornless condition can exclude the requirement of physical dehorning. (Gottardo et al. 2011; Tompson et al. 2017).

The global human population is increasing exponentially with time, and it is expected to reach 9.6 billion by 2050 (UN 2013). With rapid population growth, food consumption is predicted to more than double in the next 40 years. Due to limited land resources for cultivation and a stabilising trend in agricultural productivity, the role of livestock sector is increasing significantly to meet the rising food demand. Meanwhile, global livestock production has increased significantly in both number and productivity. Unprecedented urbanisation has a significant impact on food consumption patterns in general, as well as demand for livestock products. Animal diseases are a significant factor in reducing livestock productivity, particularly in developed countries. The limitations of traditional animal breeding hinder further improvement of livestock productivity. The use of biotechnological tools has major applications in improving animal health and production, increasing disease resistance, using assisted reproductive techniques, improving rapid diagnostic techniques, and improving vaccines in livestock systems. These interventions cover advancements in feeding, nutrition, genetics, reproduction, animal health control, and overall improvement of animal husbandry. Since genomic tools and selection have been widely adopted in developed countries to improve both productivity and quality of animal products, the prospects for increasing productivity in developing countries using genomic tools and selection have not been adequately investigated.

Introduction Wool which obtained primarily from sheep is an essential textile fiber that is used across the world. Whether for clothing, canvas work, carpets, or even upholstery, wool is a vital resource supporting many industries and occupations. This natural fiber has many benefits, such as being biodegradable, easy to clean and even having a natural structure that traps air which is excellent for heat retention. The annual wool production is 2 million tones. China ranks first in terms of global wool production. With a production of more than 333,624 tonnes of wool in 2020, China accounts for approximately 19% of the global wool clip. Australia produces 16% of global wool and is by far the largest wool exporter in the world. There has been a constant increase in the demand of wool products worldwide. This has made scientists to try to improve livestock and livestock associated derivatives. The conventional system along with the recent biotechnological interventions will be helpful for enhancing wool quality and production.

Livestock is one of the fastest growing agricultural subsectors in developing countries. In India has a vast diversity of animal genetic resources. Among them, sheep is an important ‘five star’ livestock species contributing significantly to rural economy through production of mutton, wool, manure, skin and milk etc. Sheep make a valuable contribution to the livelihood of the economically weaker sections of the society. The total Livestock population is 535.78 million and total sheep population is 74.26 million in in the country showing an increase of 4.6% and 14.1%, respectively over Livestock Census-2012. And it’s ranking 3rd in the world after China and Australia. Rajasthan is the fourth largest sheep rearing state in India with 7.9 million population (Livestock Census, 2019). Malpura sheep is one amongst the heaviest sheep breed of India, widely distributed in the semi-arid region of Rajasthan, mostly Tonk, Jaipur and Sawai Madhopur districts. Malpura sheep is reared and improved for mutton production since year 1975 at CSWRI, Avikanagar. The objective since then has been to improve the growth characteristics of this sheep breed for producing more carcass yield.The sheep is known for its adaptability to the harsh environment and potential for high meat production.Coat colour of Malpura sheep is white with coarse wool. The face is light brown with short, blunt ears. Animals have square compact body and long legs. Farmers also have liking for the long tail of animals for aesthetic purpose. The farmers like Malpura sheep for its properties like good mothering ability and produce sufficient milk to sustain and accelerate the growth of their neonates (Mishra et al., 2005), higher weight gain, number of lambs weaned, wool quality, walking capacity and the ease of management.

Livestock breeding for genetic improvement faces many challenges including feed conversion efficiency, fertility and adaptation to changing climate. These improvements have to be realized while maintaining or improving the nutritional properties of meat, milk and other animal products while giving emphasis to animal health and wellbeing also. Environmental stresses induced by climate change affect livestock productivity, reproductive efficiency and health, resulting in severe economic losses. Under the current climate change scenario, the viability of producing animals for human consumption is in jeopardy. In order to lessen the negative effects of heat stress in today’s world, it is urgently necessary to develop methods that are centered on breeding animals with increased thermal tolerance and climate resilience. The breeding of animals having genetic potential to sustain under changing environment conditions can be achieved through the various molecular genetics tools.

Introduction Estimated breeding values (EBVs), often estimated for sires, are used in traditional animal breeding (TAB) programmes to make selections based on an animal’s performance and that of its relatives. Using, typically, the best linear unbiased projections, EBVs are calculated using attributes measured in own performance, pedigree, sib, and/or progeny evaluation schemes in specialised testing stations and/or selected farm conditions (BLUP; Henderson, 1975). One of the main time-consuming challenges for phenotypic evaluation to enable a meaningful estimation of breeding values is generation intervals of 4 to 5 years in beef cattle, more than 5 years in dairy cattle, and 2 to 2.5 years in pig breeding schemes (Schefers and Weigel, 2012). Modern breeding techniques like marker-assisted selection (MAS) and, more recently, genomic selection (GS) are based on the use of genetic DNA markers to aid in selection and breeding (Williams, 2005). The fundamental reason why MAS, that only further uses a few markers as a selection tool, hasn’t made as much progress as was initially anticipated is that finding trustworthy markers is challenging, particularly when dealing with complex traits. Only relatively few consistent markers could be identified, despite successful identification of many quantitative trait loci (QTL; Hu and Reecy, 2007)

Biostatistics is a branch of statistics that focuses on the analysis and interpretation of data from biological and health-related research studies. It involves the application of statistical methods to design, collect, analyze, and interpret data from studies involving living organisms like: animals, birds including human beings. Biostatistics plays a crucial role in public health, medicine, epidemiology, genetics, and other related fields. With the increasing availability of data and advancements in biostatistics, breeders can expect further improvements in breeding practices that will results in better outcomes for both livestock farmers and the animals they breed. Animal breeding is a crucial aspect of agriculture and food production, as it helps to improve the quality and quantity of animal products as well ensuring food security to the masses.

The term “artificial intelligence” (AI) refers to a wide variety of theories and technologies that are used to the resolution of issues characterized by a high level of logical or algorithmic complexity. It cuts across a wide range of f ields, such as mechanical modeling, software engineering, data science, and statistics. Many artificial intelligence (AI) technologies, which first appeared in the 1950s, have lately been refined or extended thanks to advances in computer performance. Recent advancements have been made possible by the creation of interfaces between artificial intelligence and other fields of study, such as biomedicine, and by the collection of enormous amounts of data from a variety of fields, notably those related with the healthcare industry. First, Artificial intelligence helps with situational awareness, which is relevant in animal health and animal husbandry because it allows for the detection of patterns like repeated sequences of observations, forms like a protein, and signals like increased mortality especially in comparison to a baseline at various scales. A third option is computer-based decision making, or more practically, human decision support via knowledge - based systems, diagnostic support, and resource allocation, for instance.

Abnormal: 39, 114, 132, 140 Abnormalities: 49, 131, 145, 220 Absence: 13, 16, 22, 90, 124 Academic: 18, 132, 176, 258, 261, 263 Accelerate: 131, 201, 215, 226, 238, 263 Accessibility: 19, 39, 135, 141, 259 Accessible: 24, 40, 75, 141, 143, 145, 149, 189, 237 Accomplished: 3, 20, 127, 185, 240 Accuracies: 3, 6, 7, 13, 18, 39, 49, 182, 235, 239 Accuracy: 4, 6, 7, 8, 9, 12, 13, 15, 16, 18, 22, 23, 24, 29, 30, 34, 40, 42, 44, 49, 85, 99, 100, 102, 103, 105, 107, 110, 111, 118, 129, 136, 142, 143, 144, 145, 160, 161, 163, 164, 165, 166, 170, 171, 172, 176, 180, 182, 183, 184, 185, 186, 189, 190, 230, 231, 234, 235, 236, 237, 239, 241, 242, 248, 256